Overview

Brought to you by YData

Dataset statistics

Number of variables34
Number of observations72828
Missing cells0
Missing cells (%)0.0%
Duplicate rows13254
Duplicate rows (%)18.2%
Total size in memory29.9 MiB
Average record size in memory430.6 B

Variable types

Numeric22
Categorical12

Alerts

num_outbound_cmds has constant value "0"Constant
is_host_login has constant value "0"Constant
Dataset has 13254 (18.2%) duplicate rowsDuplicates
diff_srv_rate is highly overall correlated with dst_host_count and 6 other fieldsHigh correlation
dst_host_count is highly overall correlated with diff_srv_rate and 2 other fieldsHigh correlation
dst_host_rerror_rate is highly overall correlated with dst_host_srv_rerror_rate and 1 other fieldsHigh correlation
dst_host_same_src_port_rate is highly overall correlated with dst_host_countHigh correlation
dst_host_same_srv_rate is highly overall correlated with diff_srv_rate and 7 other fieldsHigh correlation
dst_host_serror_rate is highly overall correlated with diff_srv_rate and 5 other fieldsHigh correlation
dst_host_srv_count is highly overall correlated with diff_srv_rate and 5 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly overall correlated with dst_host_rerror_rate and 1 other fieldsHigh correlation
dst_host_srv_serror_rate is highly overall correlated with diff_srv_rate and 4 other fieldsHigh correlation
flag is highly overall correlated with logged_inHigh correlation
hot is highly overall correlated with is_guest_login and 1 other fieldsHigh correlation
is_guest_login is highly overall correlated with hotHigh correlation
land is highly overall correlated with outcomeHigh correlation
logged_in is highly overall correlated with dst_host_same_srv_rate and 3 other fieldsHigh correlation
num_compromised is highly overall correlated with hotHigh correlation
outcome is highly overall correlated with land and 3 other fieldsHigh correlation
protocol_type is highly overall correlated with outcome and 1 other fieldsHigh correlation
rerror_rate is highly overall correlated with dst_host_rerror_rate and 1 other fieldsHigh correlation
root_shell is highly overall correlated with su_attemptedHigh correlation
serror_rate is highly overall correlated with diff_srv_rate and 5 other fieldsHigh correlation
src_bytes is highly overall correlated with diff_srv_rate and 5 other fieldsHigh correlation
srv_count is highly overall correlated with protocol_typeHigh correlation
su_attempted is highly overall correlated with root_shellHigh correlation
wrong_fragment is highly overall correlated with outcomeHigh correlation
flag is highly imbalanced (55.8%)Imbalance
land is highly imbalanced (99.6%)Imbalance
wrong_fragment is highly imbalanced (95.0%)Imbalance
root_shell is highly imbalanced (98.5%)Imbalance
su_attempted is highly imbalanced (99.5%)Imbalance
num_shells is highly imbalanced (99.7%)Imbalance
is_guest_login is highly imbalanced (92.4%)Imbalance
outcome is highly imbalanced (58.9%)Imbalance
src_bytes is highly skewed (γ1 = 259.722746)Skewed
num_failed_logins is highly skewed (γ1 = 49.73817286)Skewed
num_compromised is highly skewed (γ1 = 174.9976236)Skewed
num_root is highly skewed (γ1 = 171.7050287)Skewed
num_file_creations is highly skewed (γ1 = 54.76947873)Skewed
num_access_files is highly skewed (γ1 = 45.63645767)Skewed
duration has 67035 (92.0%) zerosZeros
src_bytes has 28573 (39.2%) zerosZeros
hot has 71330 (97.9%) zerosZeros
num_failed_logins has 72747 (99.9%) zerosZeros
num_compromised has 72123 (99.0%) zerosZeros
num_root has 72428 (99.5%) zerosZeros
num_file_creations has 72658 (99.8%) zerosZeros
num_access_files has 72605 (99.7%) zerosZeros
serror_rate has 50126 (68.8%) zerosZeros
rerror_rate has 63523 (87.2%) zerosZeros
diff_srv_rate has 43977 (60.4%) zerosZeros
srv_diff_host_rate has 56527 (77.6%) zerosZeros
dst_host_same_srv_rate has 3926 (5.4%) zerosZeros
dst_host_same_src_port_rate has 36591 (50.2%) zerosZeros
dst_host_serror_rate has 46954 (64.5%) zerosZeros
dst_host_srv_serror_rate has 49363 (67.8%) zerosZeros
dst_host_rerror_rate has 59674 (81.9%) zerosZeros
dst_host_srv_rerror_rate has 61716 (84.7%) zerosZeros

Reproduction

Analysis started2024-10-25 11:59:13.152034
Analysis finished2024-10-25 12:00:23.248977
Duration1 minute and 10.1 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

duration
Real number (ℝ)

ZEROS 

Distinct1425
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.61953
Minimum0
Maximum42908
Zeros67035
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:23.334646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum42908
Range42908
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2582.9554
Coefficient of variation (CV)9.1393381
Kurtosis158.94455
Mean282.61953
Median Absolute Deviation (MAD)0
Skewness11.994575
Sum20582615
Variance6671658.7
MonotonicityNot monotonic
2024-10-25T14:00:23.486588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67035
92.0%
1 1113
 
1.5%
2 501
 
0.7%
3 330
 
0.5%
4 206
 
0.3%
5 202
 
0.3%
6 116
 
0.2%
27 108
 
0.1%
28 100
 
0.1%
10 73
 
0.1%
Other values (1415) 3044
 
4.2%
ValueCountFrequency (%)
0 67035
92.0%
1 1113
 
1.5%
2 501
 
0.7%
3 330
 
0.5%
4 206
 
0.3%
5 202
 
0.3%
6 116
 
0.2%
7 68
 
0.1%
8 47
 
0.1%
9 52
 
0.1%
ValueCountFrequency (%)
42908 1
< 0.1%
42746 1
< 0.1%
42679 1
< 0.1%
42658 2
< 0.1%
42616 1
< 0.1%
42569 1
< 0.1%
42492 1
< 0.1%
42428 1
< 0.1%
42406 1
< 0.1%
42384 2
< 0.1%

protocol_type
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
tcp
59207 
udp
8785 
icmp
 
4836

Length

Max length4
Median length3
Mean length3.066403
Min length3

Characters and Unicode

Total characters223320
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtcp
2nd rowudp
3rd rowudp
4th rowtcp
5th rowudp

Common Values

ValueCountFrequency (%)
tcp 59207
81.3%
udp 8785
 
12.1%
icmp 4836
 
6.6%

Length

2024-10-25T14:00:23.607532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-25T14:00:23.741471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
tcp 59207
81.3%
udp 8785
 
12.1%
icmp 4836
 
6.6%

Most occurring characters

ValueCountFrequency (%)
p 72828
32.6%
c 64043
28.7%
t 59207
26.5%
u 8785
 
3.9%
d 8785
 
3.9%
i 4836
 
2.2%
m 4836
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223320
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 72828
32.6%
c 64043
28.7%
t 59207
26.5%
u 8785
 
3.9%
d 8785
 
3.9%
i 4836
 
2.2%
m 4836
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223320
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 72828
32.6%
c 64043
28.7%
t 59207
26.5%
u 8785
 
3.9%
d 8785
 
3.9%
i 4836
 
2.2%
m 4836
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223320
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 72828
32.6%
c 64043
28.7%
t 59207
26.5%
u 8785
 
3.9%
d 8785
 
3.9%
i 4836
 
2.2%
m 4836
 
2.2%

flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
SF
43298 
S0
20191 
REJ
6488 
RSTR
 
1364
RSTO
 
891
Other values (6)
 
596

Length

Max length6
Median length2
Mean length2.1550914
Min length2

Characters and Unicode

Total characters156951
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSH
2nd rowSF
3rd rowSF
4th rowS0
5th rowSF

Common Values

ValueCountFrequency (%)
SF 43298
59.5%
S0 20191
27.7%
REJ 6488
 
8.9%
RSTR 1364
 
1.9%
RSTO 891
 
1.2%
S1 245
 
0.3%
SH 147
 
0.2%
S2 72
 
0.1%
RSTOS0 67
 
0.1%
S3 36
 
< 0.1%

Length

2024-10-25T14:00:23.852385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf 43298
59.5%
s0 20191
27.7%
rej 6488
 
8.9%
rstr 1364
 
1.9%
rsto 891
 
1.2%
s1 245
 
0.3%
sh 147
 
0.2%
s2 72
 
0.1%
rstos0 67
 
0.1%
s3 36
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 66378
42.3%
F 43298
27.6%
0 20258
 
12.9%
R 10174
 
6.5%
E 6488
 
4.1%
J 6488
 
4.1%
T 2351
 
1.5%
O 987
 
0.6%
1 245
 
0.2%
H 176
 
0.1%
Other values (2) 108
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 156951
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 66378
42.3%
F 43298
27.6%
0 20258
 
12.9%
R 10174
 
6.5%
E 6488
 
4.1%
J 6488
 
4.1%
T 2351
 
1.5%
O 987
 
0.6%
1 245
 
0.2%
H 176
 
0.1%
Other values (2) 108
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 156951
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 66378
42.3%
F 43298
27.6%
0 20258
 
12.9%
R 10174
 
6.5%
E 6488
 
4.1%
J 6488
 
4.1%
T 2351
 
1.5%
O 987
 
0.6%
1 245
 
0.2%
H 176
 
0.1%
Other values (2) 108
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 156951
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 66378
42.3%
F 43298
27.6%
0 20258
 
12.9%
R 10174
 
6.5%
E 6488
 
4.1%
J 6488
 
4.1%
T 2351
 
1.5%
O 987
 
0.6%
1 245
 
0.2%
H 176
 
0.1%
Other values (2) 108
 
0.1%

src_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2400
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18221.841
Minimum0
Maximum6.2156866 × 108
Zeros28573
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:24.010715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44
Q3276
95-th percentile1480
Maximum6.2156866 × 108
Range6.2156866 × 108
Interquartile range (IQR)276

Descriptive statistics

Standard deviation2335226.1
Coefficient of variation (CV)128.15534
Kurtosis68944.986
Mean18221.841
Median Absolute Deviation (MAD)44
Skewness259.72275
Sum1.3270602 × 109
Variance5.4532811 × 1012
MonotonicityNot monotonic
2024-10-25T14:00:24.161970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28573
39.2%
8 2125
 
2.9%
1 1415
 
1.9%
44 1390
 
1.9%
45 1219
 
1.7%
1032 1218
 
1.7%
46 745
 
1.0%
43 737
 
1.0%
105 602
 
0.8%
147 542
 
0.7%
Other values (2390) 34262
47.0%
ValueCountFrequency (%)
0 28573
39.2%
1 1415
 
1.9%
4 1
 
< 0.1%
5 18
 
< 0.1%
6 84
 
0.1%
7 71
 
0.1%
8 2125
 
2.9%
9 113
 
0.2%
10 108
 
0.1%
11 29
 
< 0.1%
ValueCountFrequency (%)
621568663 1
 
< 0.1%
89581520 1
 
< 0.1%
21945520 1
 
< 0.1%
18828976 1
 
< 0.1%
11396904 3
 
< 0.1%
7847476 1
 
< 0.1%
7248552 1
 
< 0.1%
5135678 8
 
< 0.1%
5133876 21
< 0.1%
5131424 4
 
< 0.1%

land
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
72807 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72828
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72807
> 99.9%
1 21
 
< 0.1%

Length

2024-10-25T14:00:24.290566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-25T14:00:24.412112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 72807
> 99.9%
1 21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 72807
> 99.9%
1 21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72807
> 99.9%
1 21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72807
> 99.9%
1 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72807
> 99.9%
1 21
 
< 0.1%

wrong_fragment
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
72190 
3
 
516
1
 
122

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72828
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72190
99.1%
3 516
 
0.7%
1 122
 
0.2%

Length

2024-10-25T14:00:24.507121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-25T14:00:24.627583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 72190
99.1%
3 516
 
0.7%
1 122
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 72190
99.1%
3 516
 
0.7%
1 122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72190
99.1%
3 516
 
0.7%
1 122
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72190
99.1%
3 516
 
0.7%
1 122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72190
99.1%
3 516
 
0.7%
1 122
 
0.2%

hot
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20290273
Minimum0
Maximum44
Zeros71330
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:24.724136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum44
Range44
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1411296
Coefficient of variation (CV)10.552493
Kurtosis159.25915
Mean0.20290273
Median Absolute Deviation (MAD)0
Skewness12.43773
Sum14777
Variance4.5844358
MonotonicityNot monotonic
2024-10-25T14:00:24.833232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 71330
97.9%
2 585
 
0.8%
1 204
 
0.3%
30 158
 
0.2%
28 146
 
0.2%
6 83
 
0.1%
4 72
 
0.1%
5 49
 
0.1%
24 43
 
0.1%
19 38
 
0.1%
Other values (12) 120
 
0.2%
ValueCountFrequency (%)
0 71330
97.9%
1 204
 
0.3%
2 585
 
0.8%
3 22
 
< 0.1%
4 72
 
0.1%
5 49
 
0.1%
6 83
 
0.1%
7 7
 
< 0.1%
9 1
 
< 0.1%
11 4
 
< 0.1%
ValueCountFrequency (%)
44 2
 
< 0.1%
30 158
0.2%
28 146
0.2%
24 43
 
0.1%
22 24
 
< 0.1%
21 2
 
< 0.1%
20 8
 
< 0.1%
19 38
 
0.1%
18 26
 
< 0.1%
15 2
 
< 0.1%

num_failed_logins
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0016751799
Minimum0
Maximum5
Zeros72747
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:24.937082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.059956322
Coefficient of variation (CV)35.790976
Kurtosis3024.3313
Mean0.0016751799
Median Absolute Deviation (MAD)0
Skewness49.738173
Sum122
Variance0.0035947606
MonotonicityNot monotonic
2024-10-25T14:00:25.037797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 72747
99.9%
1 60
 
0.1%
3 8
 
< 0.1%
2 8
 
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 72747
99.9%
1 60
 
0.1%
2 8
 
< 0.1%
3 8
 
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 3
 
< 0.1%
3 8
 
< 0.1%
2 8
 
< 0.1%
1 60
 
0.1%
0 72747
99.9%

logged_in
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
44199 
1
28629 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72828
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44199
60.7%
1 28629
39.3%

Length

2024-10-25T14:00:25.151502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-25T14:00:25.267099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 44199
60.7%
1 28629
39.3%

Most occurring characters

ValueCountFrequency (%)
0 44199
60.7%
1 28629
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 44199
60.7%
1 28629
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 44199
60.7%
1 28629
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 44199
60.7%
1 28629
39.3%

num_compromised
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40609381
Minimum0
Maximum7479
Zeros72123
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:25.372264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7479
Range7479
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.408666
Coefficient of variation (CV)99.505742
Kurtosis32234.31
Mean0.40609381
Median Absolute Deviation (MAD)0
Skewness174.99762
Sum29575
Variance1632.8603
MonotonicityNot monotonic
2024-10-25T14:00:25.503882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 72123
99.0%
1 542
 
0.7%
2 53
 
0.1%
3 21
 
< 0.1%
4 16
 
< 0.1%
5 13
 
< 0.1%
6 6
 
< 0.1%
345 4
 
< 0.1%
7 3
 
< 0.1%
622 3
 
< 0.1%
Other values (34) 44
 
0.1%
ValueCountFrequency (%)
0 72123
99.0%
1 542
 
0.7%
2 53
 
0.1%
3 21
 
< 0.1%
4 16
 
< 0.1%
5 13
 
< 0.1%
6 6
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
7479 2
< 0.1%
884 1
 
< 0.1%
809 2
< 0.1%
716 1
 
< 0.1%
691 1
 
< 0.1%
622 3
< 0.1%
558 1
 
< 0.1%
543 2
< 0.1%
462 2
< 0.1%
457 1
 
< 0.1%

root_shell
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
72732 
1
 
96

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72828
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72732
99.9%
1 96
 
0.1%

Length

2024-10-25T14:00:25.625528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-25T14:00:25.753758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 72732
99.9%
1 96
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 72732
99.9%
1 96
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72732
99.9%
1 96
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72732
99.9%
1 96
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72732
99.9%
1 96
 
0.1%

su_attempted
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
72780 
2
 
35
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72828
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72780
99.9%
2 35
 
< 0.1%
1 13
 
< 0.1%

Length

2024-10-25T14:00:25.849000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-25T14:00:25.962748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 72780
99.9%
2 35
 
< 0.1%
1 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 72780
99.9%
2 35
 
< 0.1%
1 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72780
99.9%
2 35
 
< 0.1%
1 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72780
99.9%
2 35
 
< 0.1%
1 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72780
99.9%
2 35
 
< 0.1%
1 13
 
< 0.1%

num_root
Real number (ℝ)

SKEWED  ZEROS 

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43203164
Minimum0
Maximum7468
Zeros72428
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:26.073220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7468
Range7468
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.631349
Coefficient of variation (CV)94.047161
Kurtosis31355.495
Mean0.43203164
Median Absolute Deviation (MAD)0
Skewness171.70503
Sum31464
Variance1650.9065
MonotonicityNot monotonic
2024-10-25T14:00:26.212996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 72428
99.5%
1 163
 
0.2%
6 80
 
0.1%
9 77
 
0.1%
2 15
 
< 0.1%
4 9
 
< 0.1%
5 7
 
< 0.1%
3 6
 
< 0.1%
390 4
 
< 0.1%
684 3
 
< 0.1%
Other values (29) 36
 
< 0.1%
ValueCountFrequency (%)
0 72428
99.5%
1 163
 
0.2%
2 15
 
< 0.1%
3 6
 
< 0.1%
4 9
 
< 0.1%
5 7
 
< 0.1%
6 80
 
0.1%
7 1
 
< 0.1%
9 77
 
0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
7468 2
< 0.1%
993 1
 
< 0.1%
889 2
< 0.1%
789 1
 
< 0.1%
766 1
 
< 0.1%
684 3
< 0.1%
629 1
 
< 0.1%
610 2
< 0.1%
512 2
< 0.1%
508 1
 
< 0.1%

num_file_creations
Real number (ℝ)

SKEWED  ZEROS 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012096996
Minimum0
Maximum40
Zeros72658
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:26.351138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4551408
Coefficient of variation (CV)37.624284
Kurtosis3510.0595
Mean0.012096996
Median Absolute Deviation (MAD)0
Skewness54.769479
Sum881
Variance0.20715315
MonotonicityNot monotonic
2024-10-25T14:00:26.471915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 72658
99.8%
1 94
 
0.1%
2 24
 
< 0.1%
4 7
 
< 0.1%
8 6
 
< 0.1%
15 6
 
< 0.1%
17 4
 
< 0.1%
7 3
 
< 0.1%
18 2
 
< 0.1%
3 2
 
< 0.1%
Other values (17) 22
 
< 0.1%
ValueCountFrequency (%)
0 72658
99.8%
1 94
 
0.1%
2 24
 
< 0.1%
3 2
 
< 0.1%
4 7
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 3
 
< 0.1%
8 6
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
40 2
< 0.1%
33 1
< 0.1%
29 1
< 0.1%
28 1
< 0.1%
26 2
< 0.1%
25 2
< 0.1%
22 1
< 0.1%
21 1
< 0.1%
20 1
< 0.1%
18 2
< 0.1%

num_shells
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
72802 
1
 
22
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72828
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72802
> 99.9%
1 22
 
< 0.1%
2 4
 
< 0.1%

Length

2024-10-25T14:00:26.597629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-25T14:00:26.715625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 72802
> 99.9%
1 22
 
< 0.1%
2 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 72802
> 99.9%
1 22
 
< 0.1%
2 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72802
> 99.9%
1 22
 
< 0.1%
2 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72802
> 99.9%
1 22
 
< 0.1%
2 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72802
> 99.9%
1 22
 
< 0.1%
2 4
 
< 0.1%

num_access_files
Real number (ℝ)

SKEWED  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0040506399
Minimum0
Maximum8
Zeros72605
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:26.804246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.094459433
Coefficient of variation (CV)23.319633
Kurtosis2984.7811
Mean0.0040506399
Median Absolute Deviation (MAD)0
Skewness45.636458
Sum295
Variance0.0089225845
MonotonicityNot monotonic
2024-10-25T14:00:26.904747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 72605
99.7%
1 191
 
0.3%
2 19
 
< 0.1%
3 5
 
< 0.1%
8 2
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 72605
99.7%
1 191
 
0.3%
2 19
 
< 0.1%
3 5
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 5
 
< 0.1%
2 19
 
< 0.1%
1 191
 
0.3%
0 72605
99.7%

num_outbound_cmds
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
72828 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72828
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72828
100.0%

Length

2024-10-25T14:00:27.028123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-25T14:00:27.140392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 72828
100.0%

Most occurring characters

ValueCountFrequency (%)
0 72828
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72828
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72828
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72828
100.0%

is_host_login
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
72828 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72828
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72828
100.0%

Length

2024-10-25T14:00:27.239083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-25T14:00:27.357603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 72828
100.0%

Most occurring characters

ValueCountFrequency (%)
0 72828
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72828
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72828
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72828
100.0%

is_guest_login
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
72151 
1
 
677

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72828
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72151
99.1%
1 677
 
0.9%

Length

2024-10-25T14:00:27.460429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-25T14:00:27.577568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 72151
99.1%
1 677
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 72151
99.1%
1 677
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72151
99.1%
1 677
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72151
99.1%
1 677
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72151
99.1%
1 677
 
0.9%

srv_count
Real number (ℝ)

HIGH CORRELATION 

Distinct480
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.819726
Minimum0
Maximum511
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:27.693742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median8
Q318
95-th percentile158
Maximum511
Range511
Interquartile range (IQR)16

Descriptive statistics

Standard deviation72.782732
Coefficient of variation (CV)2.6162275
Kurtosis24.173804
Mean27.819726
Median Absolute Deviation (MAD)7
Skewness4.6856117
Sum2026055
Variance5297.326
MonotonicityNot monotonic
2024-10-25T14:00:27.837381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14733
20.2%
2 7444
 
10.2%
3 3657
 
5.0%
4 3149
 
4.3%
5 2641
 
3.6%
6 2427
 
3.3%
7 2240
 
3.1%
8 2182
 
3.0%
9 2019
 
2.8%
10 1954
 
2.7%
Other values (470) 30382
41.7%
ValueCountFrequency (%)
0 6
 
< 0.1%
1 14733
20.2%
2 7444
10.2%
3 3657
 
5.0%
4 3149
 
4.3%
5 2641
 
3.6%
6 2427
 
3.3%
7 2240
 
3.1%
8 2182
 
3.0%
9 2019
 
2.8%
ValueCountFrequency (%)
511 615
0.8%
510 86
 
0.1%
509 23
 
< 0.1%
508 5
 
< 0.1%
503 1
 
< 0.1%
502 2
 
< 0.1%
499 3
 
< 0.1%
498 1
 
< 0.1%
497 1
 
< 0.1%
496 1
 
< 0.1%

serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28556393
Minimum0
Maximum1
Zeros50126
Zeros (%)68.8%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:27.969396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44699527
Coefficient of variation (CV)1.5653072
Kurtosis-1.066126
Mean0.28556393
Median Absolute Deviation (MAD)0
Skewness0.95730909
Sum20797.05
Variance0.19980477
MonotonicityNot monotonic
2024-10-25T14:00:28.102349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50126
68.8%
1 19994
 
27.5%
0.5 269
 
0.4%
0.33 192
 
0.3%
0.06 187
 
0.3%
0.07 166
 
0.2%
0.99 152
 
0.2%
0.08 139
 
0.2%
0.25 118
 
0.2%
0.01 114
 
0.2%
Other values (73) 1371
 
1.9%
ValueCountFrequency (%)
0 50126
68.8%
0.01 114
 
0.2%
0.02 57
 
0.1%
0.03 85
 
0.1%
0.04 83
 
0.1%
0.05 104
 
0.1%
0.06 187
 
0.3%
0.07 166
 
0.2%
0.08 139
 
0.2%
0.09 110
 
0.2%
ValueCountFrequency (%)
1 19994
27.5%
0.99 152
 
0.2%
0.98 37
 
0.1%
0.97 48
 
0.1%
0.96 18
 
< 0.1%
0.95 7
 
< 0.1%
0.94 14
 
< 0.1%
0.93 11
 
< 0.1%
0.92 7
 
< 0.1%
0.91 3
 
< 0.1%

rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11913303
Minimum0
Maximum1
Zeros63523
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:28.256763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31939847
Coefficient of variation (CV)2.6810238
Kurtosis3.50396
Mean0.11913303
Median Absolute Deviation (MAD)0
Skewness2.337687
Sum8676.22
Variance0.10201539
MonotonicityNot monotonic
2024-10-25T14:00:28.418698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63523
87.2%
1 7388
 
10.1%
0.93 140
 
0.2%
0.9 135
 
0.2%
0.92 131
 
0.2%
0.89 116
 
0.2%
0.5 103
 
0.1%
0.91 93
 
0.1%
0.88 81
 
0.1%
0.95 81
 
0.1%
Other values (68) 1037
 
1.4%
ValueCountFrequency (%)
0 63523
87.2%
0.01 36
 
< 0.1%
0.02 42
 
0.1%
0.03 65
 
0.1%
0.04 28
 
< 0.1%
0.05 12
 
< 0.1%
0.06 14
 
< 0.1%
0.07 16
 
< 0.1%
0.08 12
 
< 0.1%
0.09 5
 
< 0.1%
ValueCountFrequency (%)
1 7388
10.1%
0.99 8
 
< 0.1%
0.98 11
 
< 0.1%
0.97 16
 
< 0.1%
0.96 33
 
< 0.1%
0.95 81
 
0.1%
0.94 76
 
0.1%
0.93 140
 
0.2%
0.92 131
 
0.2%
0.91 93
 
0.1%

diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063142198
Minimum0
Maximum1
Zeros43977
Zeros (%)60.4%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:28.560503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.29
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.18036508
Coefficient of variation (CV)2.8564904
Kurtosis18.885304
Mean0.063142198
Median Absolute Deviation (MAD)0
Skewness4.3780524
Sum4598.52
Variance0.032531563
MonotonicityNot monotonic
2024-10-25T14:00:28.700394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43977
60.4%
0.06 11131
 
15.3%
0.07 5434
 
7.5%
0.05 4021
 
5.5%
1 2000
 
2.7%
0.08 1079
 
1.5%
0.01 559
 
0.8%
0.09 367
 
0.5%
0.04 357
 
0.5%
0.5 309
 
0.4%
Other values (77) 3594
 
4.9%
ValueCountFrequency (%)
0 43977
60.4%
0.01 559
 
0.8%
0.02 159
 
0.2%
0.03 173
 
0.2%
0.04 357
 
0.5%
0.05 4021
 
5.5%
0.06 11131
 
15.3%
0.07 5434
 
7.5%
0.08 1079
 
1.5%
0.09 367
 
0.5%
ValueCountFrequency (%)
1 2000
2.7%
0.99 21
 
< 0.1%
0.97 2
 
< 0.1%
0.96 16
 
< 0.1%
0.95 20
 
< 0.1%
0.92 3
 
< 0.1%
0.91 1
 
< 0.1%
0.88 1
 
< 0.1%
0.87 1
 
< 0.1%
0.86 1
 
< 0.1%

srv_diff_host_rate
Real number (ℝ)

ZEROS 

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.096603641
Minimum0
Maximum1
Zeros56527
Zeros (%)77.6%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:28.835954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25914335
Coefficient of variation (CV)2.6825423
Kurtosis6.9039062
Mean0.096603641
Median Absolute Deviation (MAD)0
Skewness2.8753355
Sum7035.45
Variance0.067155278
MonotonicityNot monotonic
2024-10-25T14:00:28.972221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56527
77.6%
1 4690
 
6.4%
0.01 1668
 
2.3%
0.67 570
 
0.8%
0.5 570
 
0.8%
0.12 489
 
0.7%
0.02 475
 
0.7%
0.33 472
 
0.6%
0.25 443
 
0.6%
0.11 423
 
0.6%
Other values (45) 6501
 
8.9%
ValueCountFrequency (%)
0 56527
77.6%
0.01 1668
 
2.3%
0.02 475
 
0.7%
0.03 121
 
0.2%
0.04 108
 
0.1%
0.05 178
 
0.2%
0.06 292
 
0.4%
0.07 284
 
0.4%
0.08 340
 
0.5%
0.09 364
 
0.5%
ValueCountFrequency (%)
1 4690
6.4%
0.83 4
 
< 0.1%
0.8 32
 
< 0.1%
0.75 125
 
0.2%
0.71 2
 
< 0.1%
0.67 570
 
0.8%
0.62 4
 
< 0.1%
0.6 80
 
0.1%
0.57 14
 
< 0.1%
0.56 7
 
< 0.1%

dst_host_count
Real number (ℝ)

HIGH CORRELATION 

Distinct256
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.78214
Minimum0
Maximum255
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:29.099407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q183
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)172

Descriptive statistics

Standard deviation99.006651
Coefficient of variation (CV)0.54166479
Kurtosis-1.0421691
Mean182.78214
Median Absolute Deviation (MAD)0
Skewness-0.84731409
Sum13311658
Variance9802.3169
MonotonicityNot monotonic
2024-10-25T14:00:29.240389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 43069
59.1%
1 1753
 
2.4%
2 1628
 
2.2%
3 811
 
1.1%
4 672
 
0.9%
6 414
 
0.6%
5 375
 
0.5%
7 371
 
0.5%
8 351
 
0.5%
10 323
 
0.4%
Other values (246) 23061
31.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 1753
2.4%
2 1628
2.2%
3 811
1.1%
4 672
 
0.9%
5 375
 
0.5%
6 414
 
0.6%
7 371
 
0.5%
8 351
 
0.5%
9 296
 
0.4%
ValueCountFrequency (%)
255 43069
59.1%
254 36
 
< 0.1%
253 42
 
0.1%
252 50
 
0.1%
251 55
 
0.1%
250 54
 
0.1%
249 49
 
0.1%
248 43
 
0.1%
247 42
 
0.1%
246 62
 
0.1%

dst_host_srv_count
Real number (ℝ)

HIGH CORRELATION 

Distinct256
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.50869
Minimum0
Maximum255
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:29.369077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median62
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)244

Descriptive statistics

Standard deviation110.67025
Coefficient of variation (CV)0.95811185
Kurtosis-1.7554607
Mean115.50869
Median Absolute Deviation (MAD)60
Skewness0.28667906
Sum8412267
Variance12247.903
MonotonicityNot monotonic
2024-10-25T14:00:29.494270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 20615
28.3%
1 4805
 
6.6%
2 2974
 
4.1%
3 1602
 
2.2%
4 1453
 
2.0%
254 1363
 
1.9%
5 1325
 
1.8%
20 1306
 
1.8%
6 1298
 
1.8%
19 1291
 
1.8%
Other values (246) 34796
47.8%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 4805
6.6%
2 2974
4.1%
3 1602
 
2.2%
4 1453
 
2.0%
5 1325
 
1.8%
6 1298
 
1.8%
7 1222
 
1.7%
8 1221
 
1.7%
9 1157
 
1.6%
ValueCountFrequency (%)
255 20615
28.3%
254 1363
 
1.9%
253 282
 
0.4%
252 125
 
0.2%
251 234
 
0.3%
250 180
 
0.2%
249 145
 
0.2%
248 118
 
0.2%
247 119
 
0.2%
246 108
 
0.1%

dst_host_same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52004916
Minimum0
Maximum1
Zeros3926
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:29.627919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.5
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.44867177
Coefficient of variation (CV)0.86274877
Kurtosis-1.8834592
Mean0.52004916
Median Absolute Deviation (MAD)0.49
Skewness-0.0047724314
Sum37874.14
Variance0.20130636
MonotonicityNot monotonic
2024-10-25T14:00:29.774365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 28180
38.7%
0.01 4488
 
6.2%
0 3926
 
5.4%
0.02 3792
 
5.2%
0.07 3314
 
4.6%
0.04 3103
 
4.3%
0.05 2841
 
3.9%
0.03 2340
 
3.2%
0.06 2049
 
2.8%
0.08 1622
 
2.2%
Other values (91) 17173
23.6%
ValueCountFrequency (%)
0 3926
5.4%
0.01 4488
6.2%
0.02 3792
5.2%
0.03 2340
3.2%
0.04 3103
4.3%
0.05 2841
3.9%
0.06 2049
2.8%
0.07 3314
4.6%
0.08 1622
 
2.2%
0.09 1022
 
1.4%
ValueCountFrequency (%)
1 28180
38.7%
0.99 406
 
0.6%
0.98 496
 
0.7%
0.97 278
 
0.4%
0.96 408
 
0.6%
0.95 376
 
0.5%
0.94 216
 
0.3%
0.93 274
 
0.4%
0.92 202
 
0.3%
0.91 229
 
0.3%

dst_host_same_src_port_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14796987
Minimum0
Maximum1
Zeros36591
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:29.917572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.30791963
Coefficient of variation (CV)2.0809616
Kurtosis2.7821327
Mean0.14796987
Median Absolute Deviation (MAD)0
Skewness2.0889724
Sum10776.35
Variance0.094814497
MonotonicityNot monotonic
2024-10-25T14:00:30.047191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36591
50.2%
0.01 10158
 
13.9%
1 5877
 
8.1%
0.02 3252
 
4.5%
0.03 1872
 
2.6%
0.04 1201
 
1.6%
0.05 970
 
1.3%
0.06 746
 
1.0%
0.5 659
 
0.9%
0.33 654
 
0.9%
Other values (91) 10848
 
14.9%
ValueCountFrequency (%)
0 36591
50.2%
0.01 10158
 
13.9%
0.02 3252
 
4.5%
0.03 1872
 
2.6%
0.04 1201
 
1.6%
0.05 970
 
1.3%
0.06 746
 
1.0%
0.07 620
 
0.9%
0.08 582
 
0.8%
0.09 396
 
0.5%
ValueCountFrequency (%)
1 5877
8.1%
0.99 81
 
0.1%
0.98 102
 
0.1%
0.97 77
 
0.1%
0.96 114
 
0.2%
0.95 124
 
0.2%
0.94 58
 
0.1%
0.93 96
 
0.1%
0.92 60
 
0.1%
0.91 87
 
0.1%

dst_host_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28517699
Minimum0
Maximum1
Zeros46954
Zeros (%)64.5%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:30.191042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44505213
Coefficient of variation (CV)1.5606172
Kurtosis-1.0540538
Mean0.28517699
Median Absolute Deviation (MAD)0
Skewness0.96212397
Sum20768.87
Variance0.1980714
MonotonicityNot monotonic
2024-10-25T14:00:30.324920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46954
64.5%
1 19473
26.7%
0.01 1957
 
2.7%
0.02 651
 
0.9%
0.03 418
 
0.6%
0.08 269
 
0.4%
0.09 235
 
0.3%
0.04 227
 
0.3%
0.99 163
 
0.2%
0.05 148
 
0.2%
Other values (91) 2333
 
3.2%
ValueCountFrequency (%)
0 46954
64.5%
0.01 1957
 
2.7%
0.02 651
 
0.9%
0.03 418
 
0.6%
0.04 227
 
0.3%
0.05 148
 
0.2%
0.06 127
 
0.2%
0.07 105
 
0.1%
0.08 269
 
0.4%
0.09 235
 
0.3%
ValueCountFrequency (%)
1 19473
26.7%
0.99 163
 
0.2%
0.98 90
 
0.1%
0.97 61
 
0.1%
0.96 59
 
0.1%
0.95 35
 
< 0.1%
0.94 50
 
0.1%
0.93 45
 
0.1%
0.92 25
 
< 0.1%
0.91 30
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27907865
Minimum0
Maximum1
Zeros49363
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:30.467596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44594876
Coefficient of variation (CV)1.5979322
Kurtosis-1.0142757
Mean0.27907865
Median Absolute Deviation (MAD)0
Skewness0.98855458
Sum20324.74
Variance0.1988703
MonotonicityNot monotonic
2024-10-25T14:00:30.627892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49363
67.8%
1 19847
27.3%
0.01 2131
 
2.9%
0.02 332
 
0.5%
0.03 96
 
0.1%
0.5 81
 
0.1%
0.04 58
 
0.1%
0.08 48
 
0.1%
0.05 44
 
0.1%
0.09 39
 
0.1%
Other values (88) 789
 
1.1%
ValueCountFrequency (%)
0 49363
67.8%
0.01 2131
 
2.9%
0.02 332
 
0.5%
0.03 96
 
0.1%
0.04 58
 
0.1%
0.05 44
 
0.1%
0.06 30
 
< 0.1%
0.07 37
 
0.1%
0.08 48
 
0.1%
0.09 39
 
0.1%
ValueCountFrequency (%)
1 19847
27.3%
0.98 26
 
< 0.1%
0.97 36
 
< 0.1%
0.96 27
 
< 0.1%
0.95 14
 
< 0.1%
0.94 16
 
< 0.1%
0.93 10
 
< 0.1%
0.92 9
 
< 0.1%
0.91 13
 
< 0.1%
0.9 13
 
< 0.1%

dst_host_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11837988
Minimum0
Maximum1
Zeros59674
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:30.774202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.30594402
Coefficient of variation (CV)2.5844258
Kurtosis3.7216699
Mean0.11837988
Median Absolute Deviation (MAD)0
Skewness2.3534856
Sum8621.37
Variance0.093601746
MonotonicityNot monotonic
2024-10-25T14:00:30.916409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59674
81.9%
1 5883
 
8.1%
0.01 1043
 
1.4%
0.02 687
 
0.9%
0.03 309
 
0.4%
0.05 239
 
0.3%
0.04 233
 
0.3%
0.91 188
 
0.3%
0.92 165
 
0.2%
0.89 124
 
0.2%
Other values (91) 4283
 
5.9%
ValueCountFrequency (%)
0 59674
81.9%
0.01 1043
 
1.4%
0.02 687
 
0.9%
0.03 309
 
0.4%
0.04 233
 
0.3%
0.05 239
 
0.3%
0.06 117
 
0.2%
0.07 86
 
0.1%
0.08 94
 
0.1%
0.09 45
 
0.1%
ValueCountFrequency (%)
1 5883
8.1%
0.99 40
 
0.1%
0.98 36
 
< 0.1%
0.97 53
 
0.1%
0.96 107
 
0.1%
0.95 71
 
0.1%
0.94 76
 
0.1%
0.93 55
 
0.1%
0.92 165
 
0.2%
0.91 188
 
0.3%

dst_host_srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11971879
Minimum0
Maximum1
Zeros61716
Zeros (%)84.7%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:31.048939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31885041
Coefficient of variation (CV)2.6633281
Kurtosis3.5533862
Mean0.11971879
Median Absolute Deviation (MAD)0
Skewness2.3448552
Sum8718.88
Variance0.10166559
MonotonicityNot monotonic
2024-10-25T14:00:31.192204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61716
84.7%
1 7565
 
10.4%
0.01 785
 
1.1%
0.02 339
 
0.5%
0.04 222
 
0.3%
0.03 199
 
0.3%
0.05 187
 
0.3%
0.98 130
 
0.2%
0.99 110
 
0.2%
0.06 102
 
0.1%
Other values (90) 1473
 
2.0%
ValueCountFrequency (%)
0 61716
84.7%
0.01 785
 
1.1%
0.02 339
 
0.5%
0.03 199
 
0.3%
0.04 222
 
0.3%
0.05 187
 
0.3%
0.06 102
 
0.1%
0.07 40
 
0.1%
0.08 37
 
0.1%
0.09 21
 
< 0.1%
ValueCountFrequency (%)
1 7565
10.4%
0.99 110
 
0.2%
0.98 130
 
0.2%
0.97 57
 
0.1%
0.96 57
 
0.1%
0.95 49
 
0.1%
0.94 48
 
0.1%
0.93 26
 
< 0.1%
0.92 29
 
< 0.1%
0.91 31
 
< 0.1%

level
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.513964
Minimum0
Maximum21
Zeros31
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size569.1 KiB
2024-10-25T14:00:31.329385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q118
median20
Q321
95-th percentile21
Maximum21
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2661815
Coefficient of variation (CV)0.11613127
Kurtosis13.218426
Mean19.513964
Median Absolute Deviation (MAD)1
Skewness-2.8730606
Sum1421163
Variance5.1355788
MonotonicityNot monotonic
2024-10-25T14:00:31.434228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
21 36174
49.7%
18 12039
 
16.5%
20 11177
 
15.3%
19 5966
 
8.2%
15 2260
 
3.1%
17 1796
 
2.5%
16 1339
 
1.8%
12 429
 
0.6%
14 395
 
0.5%
11 348
 
0.5%
Other values (12) 905
 
1.2%
ValueCountFrequency (%)
0 31
 
< 0.1%
1 35
 
< 0.1%
2 25
 
< 0.1%
3 42
 
0.1%
4 43
 
0.1%
5 48
0.1%
6 56
0.1%
7 67
0.1%
8 58
0.1%
9 109
0.1%
ValueCountFrequency (%)
21 36174
49.7%
20 11177
 
15.3%
19 5966
 
8.2%
18 12039
 
16.5%
17 1796
 
2.5%
16 1339
 
1.8%
15 2260
 
3.1%
14 395
 
0.5%
13 256
 
0.4%
12 429
 
0.6%

outcome
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
normal
38989 
neptune
23840 
satan
 
2094
ipsweep
 
2010
portsweep
 
1687
Other values (16)
4208 

Length

Max length15
Median length6
Mean length6.3847284
Min length3

Characters and Unicode

Total characters464987
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownmap
2nd rownormal
3rd rownormal
4th rowneptune
5th rownormal

Common Values

ValueCountFrequency (%)
normal 38989
53.5%
neptune 23840
32.7%
satan 2094
 
2.9%
ipsweep 2010
 
2.8%
portsweep 1687
 
2.3%
smurf 1564
 
2.1%
nmap 887
 
1.2%
teardrop 523
 
0.7%
back 517
 
0.7%
warezclient 487
 
0.7%
Other values (11) 230
 
0.3%

Length

2024-10-25T14:00:31.544518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
normal 38989
53.5%
neptune 23840
32.7%
satan 2094
 
2.9%
ipsweep 2010
 
2.8%
portsweep 1687
 
2.3%
smurf 1564
 
2.1%
nmap 887
 
1.2%
teardrop 523
 
0.7%
back 517
 
0.7%
warezclient 487
 
0.7%
Other values (11) 230
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 90153
19.4%
e 56685
12.2%
a 45676
9.8%
r 43852
9.4%
m 41463
8.9%
o 41379
8.9%
l 39525
8.5%
p 32816
 
7.1%
t 28668
 
6.2%
u 25465
 
5.5%
Other values (13) 19305
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 464987
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 90153
19.4%
e 56685
12.2%
a 45676
9.8%
r 43852
9.4%
m 41463
8.9%
o 41379
8.9%
l 39525
8.5%
p 32816
 
7.1%
t 28668
 
6.2%
u 25465
 
5.5%
Other values (13) 19305
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 464987
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 90153
19.4%
e 56685
12.2%
a 45676
9.8%
r 43852
9.4%
m 41463
8.9%
o 41379
8.9%
l 39525
8.5%
p 32816
 
7.1%
t 28668
 
6.2%
u 25465
 
5.5%
Other values (13) 19305
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 464987
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 90153
19.4%
e 56685
12.2%
a 45676
9.8%
r 43852
9.4%
m 41463
8.9%
o 41379
8.9%
l 39525
8.5%
p 32816
 
7.1%
t 28668
 
6.2%
u 25465
 
5.5%
Other values (13) 19305
 
4.2%

Interactions

2024-10-25T14:00:18.906691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:21.625988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:24.274306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:27.186755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:29.921103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:32.746330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:35.965055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:38.679660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:41.476731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:44.072501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:46.994209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:49.587259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:52.142601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:54.727692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:57.704426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:00.306295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:02.934537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:05.518794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:08.649004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:11.252220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:13.821322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:16.364349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:19.014545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:21.737032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:24.408436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:27.297223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:30.030692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:32.913261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:36.078608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:38.779531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:41.576526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:44.174789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:47.120744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:49.692230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:52.257768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:54.843046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:57.806232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:00.408850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:03.047754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:05.631736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:08.776109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:11.357145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:13.931814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:16.474272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:19.128105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:21.871687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:24.521494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:27.435951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:30.156557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:33.095288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:36.236789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:38.895694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:41.681324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:44.302725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:47.237710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:49.801042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:52.377206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:54.964544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:57.944629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:00.537147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:03.179038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:05.753410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:08.906237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:11.488567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:14.048081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:16.593297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:19.230648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:22.008263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:24.637315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:27.544897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:30.279575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:33.252478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:36.346494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:38.996668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:41.780578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:44.413969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:47.367758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:49.924623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:52.486751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:55.074618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:58.070257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:00.644375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:03.287206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:05.874506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:09.017673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:11.602796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:14.156155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:16.696241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:19.341126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:22.130874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:24.890257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:27.660732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:30.401216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:33.393649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:36.462951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:39.100607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:41.889552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:44.531075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:47.471036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:50.036144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:52.597491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:55.186269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:58.193849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:00.768375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:03.397730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:05.989521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:09.139674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:11.705445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:14.266206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:16.815858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:19.454067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:22.271045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:25.012483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:27.778752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:30.523949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:33.565207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:36.581161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:39.221113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:41.999570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:44.647565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:47.594889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:50.156007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:52.712405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:55.317714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:58.313923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:00.894340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:03.517199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:06.111120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:09.263674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:11.826215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:14.389734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:16.926596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:19.576539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:22.401292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:25.133581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:27.904451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:30.646708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:33.719579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:36.697629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:39.347369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:42.101089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:44.774799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-10-25T13:59:56.909716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:59.470315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:02.069066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:04.716190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:07.857292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:10.439013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:12.976373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:15.581333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:18.109419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:21.370357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:23.574115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:26.428585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:29.178760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:31.965351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:35.256317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:37.948434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:40.821101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:43.341444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:45.986169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:48.874464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:51.437709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:54.032527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:57.021199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:59.594619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:02.180966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:04.838020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:07.967549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:10.554379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:13.110424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:15.691533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:18.229524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:21.477951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:23.676629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:26.558559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:29.284457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:32.087996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:35.366463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:38.080823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:40.936293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:43.465504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:46.097248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:48.981084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:51.567231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:54.153166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:57.131942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:59.707196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:02.314232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:04.950423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:08.084783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:10.671923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:13.242414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:15.804427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:18.344441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:21.593576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:23.788671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:26.704019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:29.390121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:32.211922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:35.484537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:38.207243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:41.046244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:43.590735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:46.221095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:49.095589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:51.688826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:54.267034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:57.247483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:59.817695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:02.452655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:05.061699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:08.192589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:10.782981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:13.364825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:15.916795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:18.454299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:21.713843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:23.890189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:26.825588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:29.546334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:32.321169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:35.603811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:38.324402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:41.155631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:43.722135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:46.331063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:49.211282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:51.800817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:54.386866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:57.357178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:59.936677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:02.577734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:05.180427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:08.311675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:10.909749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:13.477909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:16.031526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:18.573783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:21.843304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:24.012088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:26.947419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:29.698782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:32.428750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:35.728565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:38.454914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:41.269537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:43.841631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:46.777563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:49.355761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:51.920698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:54.499063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:57.480615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:00.064500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:02.697695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:05.297465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:08.424535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:11.033594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:13.601210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:16.142808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:18.684223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:21.968173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:24.123962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:27.068024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:29.819577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:32.584037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:35.854615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:38.578913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:41.377535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:43.964426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:46.893981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:49.474030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:52.032389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:54.620767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T13:59:57.594374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:00.186046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:02.824697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:05.419616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:08.541007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:11.144864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:13.713127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:16.257935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-25T14:00:18.797715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-10-25T14:00:31.694897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
diff_srv_ratedst_host_countdst_host_rerror_ratedst_host_same_src_port_ratedst_host_same_srv_ratedst_host_serror_ratedst_host_srv_countdst_host_srv_rerror_ratedst_host_srv_serror_ratedurationflaghotis_guest_loginlandlevellogged_innum_access_filesnum_compromisednum_failed_loginsnum_file_creationsnum_rootnum_shellsoutcomeprotocol_typererror_rateroot_shellserror_ratesrc_bytessrv_countsrv_diff_host_ratesu_attemptedwrong_fragment
diff_srv_rate1.0000.5250.154-0.437-0.7270.641-0.6680.1590.594-0.1370.148-0.1020.0150.000-0.2120.175-0.041-0.073-0.026-0.028-0.0460.0120.2230.1220.2330.0000.670-0.703-0.037-0.3740.0000.015
dst_host_count0.5251.0000.050-0.691-0.5270.421-0.3460.0280.379-0.0720.161-0.0710.0680.040-0.1310.467-0.002-0.045-0.028-0.030-0.0600.0200.2320.2260.0800.0290.428-0.4060.224-0.3100.0150.053
dst_host_rerror_rate0.1540.0501.0000.044-0.244-0.192-0.2600.880-0.2720.0590.3280.0890.0330.000-0.1860.276-0.0100.1250.0400.003-0.0100.0230.2350.1240.8380.014-0.228-0.227-0.211-0.1370.0090.149
dst_host_same_src_port_rate-0.437-0.6910.0441.0000.302-0.4530.151-0.005-0.4500.1800.153-0.0030.0390.041-0.1220.213-0.012-0.0050.0120.0210.0640.0350.2740.435-0.0120.000-0.4840.378-0.1950.1580.0000.145
dst_host_same_srv_rate-0.727-0.527-0.2440.3021.000-0.5820.920-0.217-0.516-0.1390.2660.0610.2390.0080.2690.6280.0120.0680.003-0.007-0.0210.0210.2980.215-0.2880.026-0.5720.6150.2540.4420.0380.131
dst_host_serror_rate0.6410.421-0.192-0.453-0.5821.000-0.529-0.2050.920-0.1580.346-0.0620.0620.060-0.1790.497-0.026-0.0300.008-0.005-0.0350.0130.3180.215-0.1790.0420.936-0.6240.048-0.3300.0740.105
dst_host_srv_count-0.668-0.346-0.2600.1510.920-0.5291.000-0.246-0.471-0.1560.2520.0060.1660.0160.3790.6450.0130.010-0.026-0.021-0.0310.0180.2960.254-0.3080.010-0.5230.6170.2950.3970.0160.116
dst_host_srv_rerror_rate0.1590.0280.880-0.005-0.217-0.205-0.2461.000-0.2490.0630.3570.0880.0500.000-0.1300.281-0.0060.1440.0310.000-0.0170.0000.1810.1280.8840.098-0.205-0.260-0.218-0.1260.0900.023
dst_host_srv_serror_rate0.5940.379-0.272-0.450-0.5160.920-0.471-0.2491.000-0.1520.378-0.0680.0600.176-0.1180.494-0.025-0.0260.009-0.009-0.0260.1020.3040.213-0.2230.0870.922-0.6110.080-0.3040.1360.040
duration-0.137-0.0720.0590.180-0.139-0.158-0.1560.063-0.1521.0000.1860.2250.0130.000-0.0040.0630.0490.0730.0630.0850.0430.0000.1630.0810.0490.200-0.1850.225-0.3180.0150.2100.000
flag0.1480.1610.3280.1530.2660.3460.2520.3570.3780.1861.0000.0290.0790.0250.1570.6500.0120.0000.0590.0320.0000.0000.4320.2800.3730.0390.3900.0860.0830.1090.0260.054
hot-0.102-0.0710.089-0.0030.061-0.0620.0060.088-0.0680.2250.0291.0000.9300.000-0.1480.1140.0020.5140.0950.0590.0090.0000.1450.030-0.0110.041-0.0850.202-0.142-0.0290.0000.000
is_guest_login0.0150.0680.0330.0390.2390.0620.1660.0500.0600.0130.0790.9301.0000.0000.1710.1190.0000.0000.0200.0240.0000.0000.2870.0460.0350.0000.0590.0000.0270.0380.0000.007
land0.0000.0400.0000.0410.0080.0600.0160.0000.1760.0000.0250.0000.0001.0000.0960.0120.0000.0000.0000.0000.0000.0000.8730.0060.0000.0000.0250.0000.0000.0470.0000.000
level-0.212-0.131-0.186-0.1220.269-0.1790.379-0.130-0.118-0.0040.157-0.1480.1710.0961.0000.3600.021-0.150-0.054-0.014-0.0000.0550.3850.335-0.1450.118-0.1630.279-0.0580.1370.0420.201
logged_in0.1750.4670.2760.2130.6280.4970.6450.2810.4940.0630.6500.1140.1190.0120.3601.0000.0680.0090.0120.0280.0110.0230.7290.3860.2890.0450.4900.0020.2290.3360.0310.075
num_access_files-0.041-0.002-0.010-0.0120.012-0.0260.013-0.006-0.0250.0490.0120.0020.0000.0000.0210.0681.0000.1070.0060.0640.1080.0230.0530.016-0.0190.412-0.0340.062-0.0460.0040.4570.000
num_compromised-0.073-0.0450.125-0.0050.068-0.0300.0100.144-0.0260.0730.0000.5140.0000.000-0.1500.0090.1071.0000.0260.0980.1700.0000.0000.000-0.0030.177-0.0610.152-0.075-0.0150.2070.000
num_failed_logins-0.026-0.0280.0400.0120.0030.008-0.0260.0310.0090.0630.0590.0950.0200.000-0.0540.0120.0060.0261.0000.0580.0250.0000.3400.0080.0350.072-0.0220.014-0.041-0.0180.1140.000
num_file_creations-0.028-0.0300.0030.021-0.007-0.005-0.0210.000-0.0090.0850.0320.0590.0240.000-0.0140.0280.0640.0980.0581.0000.0810.1060.0700.000-0.0130.089-0.0230.073-0.053-0.0100.0910.000
num_root-0.046-0.060-0.0100.064-0.021-0.035-0.031-0.017-0.0260.0430.0000.0090.0000.000-0.0000.0110.1080.1700.0250.0811.0000.0000.0000.000-0.0270.228-0.0450.086-0.085-0.0270.2670.000
num_shells0.0120.0200.0230.0350.0210.0130.0180.0000.1020.0000.0000.0000.0000.0000.0550.0230.0230.0000.0000.1060.0001.0000.2650.0040.0000.1590.0000.0000.0000.0000.0000.000
outcome0.2230.2320.2350.2740.2980.3180.2960.1810.3040.1630.4320.1450.2870.8730.3850.7290.0530.0000.3400.0700.0000.2651.0000.6620.2450.3090.3090.0050.2970.2140.0030.976
protocol_type0.1220.2260.1240.4350.2150.2150.2540.1280.2130.0810.2800.0300.0460.0060.3350.3860.0160.0000.0080.0000.0000.0040.6621.0000.1270.0170.2180.0000.5470.2760.0070.191
rerror_rate0.2330.0800.838-0.012-0.288-0.179-0.3080.884-0.2230.0490.373-0.0110.0350.000-0.1450.289-0.019-0.0030.035-0.013-0.0270.0000.2450.1271.0000.003-0.174-0.362-0.206-0.1480.0000.022
root_shell0.0000.0290.0140.0000.0260.0420.0100.0980.0870.2000.0390.0410.0000.0000.1180.0450.4120.1770.0720.0890.2280.1590.3090.0170.0031.0000.0170.0000.0000.0090.6200.000
serror_rate0.6700.428-0.228-0.484-0.5720.936-0.523-0.2050.922-0.1850.390-0.0850.0590.025-0.1630.490-0.034-0.061-0.022-0.023-0.0450.0000.3090.218-0.1740.0171.000-0.6720.073-0.3260.0000.125
src_bytes-0.703-0.406-0.2270.3780.615-0.6240.617-0.260-0.6110.2250.0860.2020.0000.0000.2790.0020.0620.1520.0140.0730.0860.0000.0050.000-0.3620.000-0.6721.000-0.0560.2870.0000.000
srv_count-0.0370.224-0.211-0.1950.2540.0480.295-0.2180.080-0.3180.083-0.1420.0270.000-0.0580.229-0.046-0.075-0.041-0.053-0.0850.0000.2970.547-0.2060.0000.073-0.0561.0000.2340.0000.249
srv_diff_host_rate-0.374-0.310-0.1370.1580.442-0.3300.397-0.126-0.3040.0150.109-0.0290.0380.0470.1370.3360.004-0.015-0.018-0.010-0.0270.0000.2140.276-0.1480.009-0.3260.2870.2341.0000.0000.119
su_attempted0.0000.0150.0090.0000.0380.0740.0160.0900.1360.2100.0260.0000.0000.0000.0420.0310.4570.2070.1140.0910.2670.0000.0030.0070.0000.6200.0000.0000.0000.0001.0000.000
wrong_fragment0.0150.0530.1490.1450.1310.1050.1160.0230.0400.0000.0540.0000.0070.0000.2010.0750.0000.0000.0000.0000.0000.0000.9760.1910.0220.0000.1250.0000.2490.1190.0001.000

Missing values

2024-10-25T14:00:22.180275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-25T14:00:22.758430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationprotocol_typeflagsrc_byteslandwrong_fragmenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_loginsrv_countserror_ratererror_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_same_src_port_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateleveloutcome
00tcpSH000000000000000011.000.000.000.0014410.010.940.941.00.00.019nmap
10udpSF10500000000000000020.000.000.400.002552450.960.020.000.00.00.021normal
20udpSF3300000000000000030.000.000.000.672552551.000.010.000.00.00.021normal
30tcpS00000000000000000131.000.000.080.00255130.050.001.001.00.00.018neptune
40udpSF430000000000000002010.000.000.000.012552551.000.000.000.00.00.018normal
50tcpS00000000000000000140.980.020.090.0025550.020.011.001.00.00.018neptune
60tcpSF21000001000000000030.000.000.000.002552551.000.000.000.00.00.021normal
70tcpSF20500001000000000010.000.000.000.002552551.000.000.000.00.00.021normal
838460tcpRSTR100000000000000020.001.000.000.0025520.011.000.000.01.01.015portsweep
90tcpSF19600001000000000080.000.000.000.00272551.000.040.000.00.00.021normal
durationprotocol_typeflagsrc_byteslandwrong_fragmenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_loginsrv_countserror_ratererror_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_same_src_port_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateleveloutcome
728181tcpSF149200001000000000060.00.00.001.002441650.630.000.00.000.00.021normal
728190tcpRSTO0000000000000000190.01.00.060.00255190.070.000.00.001.01.020neptune
728200tcpSF34000001000000000070.00.00.000.00852551.000.010.00.000.00.021normal
728210tcpSF29400001000000000060.00.00.000.00772551.000.010.00.000.00.021normal
728220tcpSF33100001000000000010.00.00.000.002552551.000.000.00.000.00.021normal
728230tcpS00000000000000000231.00.00.060.00255230.090.001.01.000.00.021neptune
728240udpSF430000000000000001140.00.00.000.002551750.690.000.00.000.00.020normal
728250tcpS00000000000000000151.00.00.060.00255150.060.001.01.000.00.020neptune
728260tcpS0000000000000000041.00.00.060.0025540.020.001.01.000.00.020neptune
728270tcpS0000000000000000081.00.00.000.3842551.000.251.00.020.00.018normal

Duplicate rows

Most frequently occurring

durationprotocol_typeflagsrc_byteslandwrong_fragmenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_loginsrv_countserror_ratererror_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_same_src_port_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateleveloutcome# duplicates
47460tcpS00000000000000000191.00.00.060.0255190.070.01.01.00.00.020neptune158
48910tcpS00000000000000000201.00.00.060.0255200.080.01.01.00.00.020neptune154
46200tcpS00000000000000000181.00.00.060.0255180.070.01.01.00.00.020neptune111
46920tcpS00000000000000000191.00.00.050.0255190.070.01.01.00.00.020neptune98
33340tcpS0000000000000000081.00.00.060.025580.030.01.01.00.00.020neptune96
48280tcpS00000000000000000201.00.00.050.0255200.080.01.01.00.00.020neptune96
49790tcpS00000000000000000241.00.00.050.0255240.090.01.01.00.00.018neptune94
23970tcpS0000000000000000011.00.00.070.025510.000.01.01.00.00.019neptune93
28120tcpS0000000000000000041.00.00.060.025540.020.01.01.00.00.020neptune86
35890tcpS00000000000000000101.00.00.060.0255100.040.01.01.00.00.020neptune85